Abstract
Wikipedia is a free, multilingual, and collaborative online encyclopedia. Nowadays, it is one of the largest sources of online knowledge, often appearing at the top of the results of the major search engines, being one of the most sought-after resources by the public searching for health information. The collaborative nature of Wikipedia raises security concerns since this information is used for decision-making, especially in the health area. Despite being available in hundreds of idioms, there are asymmetries between idioms, namely regarding their quality. In this work, we compare the quality of health information on Wikipedia between idioms with 100 million native speakers or more, and also in Greek, Italian, Korean, Turkish, Persian, Catalan and Hebrew, for historical tradition. Quality metrics are applied to health and medical articles in English, maintained by WikiProject Medicine, and their versions in the above idioms. With this, we contribute to a clarification of the role of Wikipedia in the access to health information. We demonstrate differences in both the quantity and quality of information available between idioms. English is the idiom with the highest quality in general. Urdu, Greek, Indonesian, and Hindi achieved lower values of quality.

Abstract
Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.

Abstract
In the past decades, the resource on the resource-constrained project scheduling problem (RCPSP) has grown rapidly, resulting in an overwhelming amount of solution procedures that provide (near)-optimal solutions in a reasonable time. Despite the rapid progress, little is still known what makes a project instance hard to solve. Inspired by a previous research study that has shown that even small instances with only up to 30 activities is sometimes hard to solve, the current study provides an analysis of the project data used in the academic literature. More precisely, it investigates the ability of four well-known resource indicators to predict the hardness of an RCPSP instance. The study introduces a new instance equivalence concept to show that instances might have very different values for their resource indicators without changing any possible solution for this instance. The concept is based on four theorems and a search algorithm that transforms existing instances into new equivalent instances with more compact resources. This algorithm illustrates that the use of resource indicators to predict the hardness of an instance is sometimes misleading. In a set of computational experiment on more than 10,000 instances, it is shown that the newly constructed equivalent instances have values for the resource indicators that are not only different than the values of the original instances, but also often are better in predicting the hardness the project instances. It is suggested that the new equivalent instances are used for further research to compare results on the new instances with results obtained from the original dataset.

Abstract
Telecoms are companies that provide communication services. In 2020 a drop in Telecom's revenues is expected due to COVID and this impact will remain for future years, with higher drops in coming years. The adoption of Self-Service Technologies allows to solve the problem of selling and serve the customers on-line, increasing customer loyalty, reducing cost, fostering innovation and thus increasing revenues. The major research question for this work is: Which factors influence the adoption of online self-service technologies by Business-to-Business customers of a Telecom?. In order to derive a conceptual framework to answer this question several theories where used: The Technology Acceptance Model (TAM), The Innovation Diffusion Theory (IDT) and the SST attitude/intention model. A conceptual framework that drives the adoption of Self-service technologies in Telecom B2B segments was proposed and tested.

Abstract
This paper aims to define a problem to be studied and summarize previous research work done. Remote work or telework enterprise model has been a growing trend. The Covid-19 pandemic forced businesses to adopt a temporary remote-working mode. Now managers are investigating whether this dnew working-mode is, or not, a better approach than the traditional working mode known as the office model. The proposed framework will support the transformation process from a physical into a remote work model supported by applying digital tools and methods. Furthermore, the framework proposed in this work aims to help business decision makers to decide which organizational model they should adopt after Covid-19: full-remote; hybrid or partially remote; or physical, also known as going back to the office. A simple and pragmatic framework is proposed to help organizations: understand if it's possible to transform a business from a physical (at the office) to a full or partial remote model; define the main goals of this transformation; create a simple template to map the new digital enterprise architecture in terms of tools, data management, business processes and human resources (job functions and digital skills).

Abstract
In a competitive business environment, strongly supported on Information and Communication Technologies (ICT), organizations increasingly need to be prepared to cope with disruptions in their activity and business processes. Business Continuity Management (BCM) encompasses effective planning for the relaunch of business processes in the short term, through the implementation of a Business Continuity Plan (BCP), which constitutes a decisive management factor for the continuity of value creation or guarantee of delivery of goods or services, to safeguard the business survival. This work addresses this issue, supported by a preliminary literature review oriented to identify and relate the common basis of components and activities of the BCM in the normative references, models and libraries of good practices, in order to explore the identification of its gaps in driving an achievable instrument to all organization sizes, considering each component of the BCM, allowing to assess the stage of preparedness, implementation and appraisal of the essential elements, with greater focus on ICT systems, that guide the BCM and the design of a BCP tailored to an organization.